Framework para Gen+AI segundo Gartner

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GenAI Planning
Workbook
4 steps to implementing
generative AI in your enterprise

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Generative AI (GenAI) is suddenly on everyone’s
radar, but some organizations already have
extensive experience and success in deploying
AI techniques across multiple business units
and processes.
Gartner research shows these mature AI
organizations represent just 10% of those
currently experimenting with AI, but would-be
GenAI adopters can learn a lot from them.
Use this planning workbook to focus
conversations among business and IT leaders
around best practices that help you focus on
GenAI initiatives that are both valuable and
feasible. To get there, take a strategic approach.
Focus GenAI conversations on real business
problems and achievable use cases

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Actions related to the 4 pillars of GenAI strategy
Establish your vision for GenAI
How GenAI will drive your
enterprise goals, what benefits
you expect and how you will
measure success.
Remove barriers to
capturing value
What organizational barriers
could hinder your success and
what actions are needed to
remove those hurdles.
Identify the risks
What regulatory, reputational,
competency, technology and
other risks you may need to
assess and mitigate.
Prioritize adoption
Which are the best GenAI
initiatives to pursue, based on
their value and their feasibility —
as agreed to by both IT and
business leaders.

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Risks
• Regulatory
• Reputational
• Competency
Vision
• Goals
• Benefits
• Success metrics
Value
• Business impact
• Change
management
• People and skills
Adoption
• Use cases and
value maps
• AI decision
framework
• Decision
governance
Key components of your AI strategy framework

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Don’t underestimate the need to level-set with
stakeholders from the outset:
1. Restate the corporate vision of your
enterprise:
“……………………………………………”
2. State how AI will support that vision:
– e.g., AI will enable better business value
in these areas in these ways
– e.g., We will use AI to achieve fairer
outcomes
First, state clearly how GenAI objectives
link to enterprise goals
Stating AI goals clearly is key
to encouraging and enabling
organizationwide fluency and
adoption of AI. It will also help
you to fund the right use cases —
ones that will deliver clear return
on investment and lead to further
innovation.
AI VISION

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Ask why you are pursuing GenAI and what value you expect it to bring based on your major business goals, how you will
measure success and what use cases could maximize that value (you will verify the value/feasibility of those use cases in the
“adoption” phase).
Goal How AI/GenAI Enables That Goal
Use Cases to Pursue
(Illustrative Examples)
Topline revenue growth Business model change inspired or supported by AI creates
net-new business initiatives.
Behavioral analytics, contract life cycle
management
Improved customer
satisfaction
Greater ability to conduct customer behavior analytics
increases proximity to the customer.
Virtual customer assistants
Reduced costs Task and process automation reduce operational costs. Risk/fraud mitigation, asset performance
management
Staff augmentation and
increased productivity
Augmented AI and automation increase productivity by shifting
people away from managing mundane tasks.
Knowledge management and training, content
generation, code generation
Improved service
availability
Data-driven predictive analytics tools advance digital services. Predictive maintenance, proactive threat
management
Then, specify how GenAI will drive business goals
Illustrative
AI VISION

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Also, set AI success metrics
To measure the value of individual use cases, you’ll need success metrics that tie into your overarching business
goal. Select metrics like those listed here that relate to specific key success factors and provide a timeframe in
which you expect to demonstrate value.
Business Goal Appropriate Success Metric Completion
Improved customer satisfaction Customer satisfaction index/Net Promoter Score Date
Topline revenue growth Revenue growth for product lines Date
New business initiatives Number of new business initiatives Date
Task or process automation Reduction in processing time Date
Reduce costs Reduction in CapEx and OpEx Date
Staff augmentation and increased
productivity
Workforce productivity metrics, such as time spent on value-added tasks Date
Improved service availability % of annual availability Date
AI VISION

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Having identified potential benefits to the business (in the vision stage), surface any strategic concerns that could hinder your
ability to capture value in the way you have identified it. Also, identify solutions, responsibilities and actions as illustrated here.
Remove organizational barriers to capturing value
Strategic Concern Solution
Executive(s)
Responsible
What the Organization
Will Do
Projects aligned to corporate
goals are more likely to succeed
and mature.
Document goals and require a
portfolio approach to AI opportunity.
CIO • Indicate which corporate goals should be
addressed.
• Size portfolio (five or fewer pilots and
minimum viable products).
Metrics deliver credibility for
project maturity.
Select metrics as proxies for financial
and risk results or direct such
measurements.
CFO • Collaborate with your chief data and analytics
officer to discuss what will be most
measurable and educational for future
projects.
Formal structures of accountability
bolster AI results.
Help complete a RACI (responsible,
accountable, consulted and
informed) matrix for AI strategy
development and execution.
Chief data (and
analytics) officers,
CIO
• Draft a RACI matrix for all aspects of AI
project and product development.
AI VALUE

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Key Types
of Risks
Risk
Category
Executive(s)
Responsible
Action Plan
Regulatory Adhere to
regulations
CIO/CTO and
CRO
Understand the continuously
evolving regulatory landscape.
Enable collaboration between
AI practitioners and legal, risk
and security members to
evaluate use case feasibility
and acceptable risks.
Create an AI governance
office, which serves an
independent audit committee
to review results.
Reputational Secure and
safe
CIO/CTO Acknowledge the threats
against AI posed by both
malicious and benign actors
in your organization.
Bolster security across
enterprise security controls,
data integrity and AI model
monitoring.
Leverage external resources
to help secure your AI
systems.
Competencies Technical debt CIO/CTO Align AI strategy with cloud
strategy and explore cloud
as foundation for AI.
Create a technology roadmap
to modernize data and
analytics infrastructures to
align with AI goals and
timeline.
Create a startup accelerator
program to reduce technical
debt and innovate
incrementally.
Assess and mitigate risks
Any type of AI comes with a range of risks, including those illustrated here. GenAI carries specific new types of risks, such
as hallucinations and biased and inaccurate results. Log all such major risks so you can properly assess and mitigate each.
AI RISKS

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Rate the feasibility and value of each project using simple criteria like those shown here, and actually score each so you can rank projects
against one another. Typically, executives are keen to pursue initiatives where value is high (and risk also tends to be high, i.e., feasibility is
low) but avoid projects where feasibility is so low that it makes the project impossible. A use case with a seemingly outstanding contribution
to business value and strong feasibility is either a breakthrough, or the market is missing a great opportunity.
Prioritize projects that are valuable and feasible
TECHNICAL FEASIBILITY FACTORS BUSINESS VALUE FACTORS
Project
Access to
Labeled Data
Architecture
and Technology
Feasibility
Have Skills/
People to
Execute
Aligns With
Our Mission
and Values
Sponsor
Support
KPIs
Measurable
Overall
Business
Value
(Scale of 1 to
10; 10 Being
High)
Overall Technical
Feasibility
(Scale of 1 to 10;
10 Being High)
Ranking
Name Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No
Name Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No
Name Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No
Name Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No
Name Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No
AI ADOPTION

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Actionable, objective insight
Position your IT organization for success. Explore these additional
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